The factors affecting the accuracy of real-time data of HPIC dose rate in nuclear radiation monitoring stations are complex, such as natural factors of rainfall, temperature and humidity, wind direction and solar radiation, objective factors of equipment anomalies and radioactivity, etc. When it is found that the radiation monitoring state is abnormal, it is difficult to analyze the cause of the deviation of the monitoring data. Combined with the monitoring data of massive historical radiation series of ERMS, the characteristics of rainfall, temperature and humidity, air pressure, wind direction, electrons in the zenith direction of solar radiation and the radiation values of surrounding sites are deeply explored. HPIC is established based on the Gradient Boosting algorithm (referred to as GB algorithm). The online prediction model of dose rate radiation data effectively combines the natural characteristic factors, reduces the natural factor's analysis of the HPIC dose rate radiation monitoring numerical anomaly and the interference effect of interpretation, and improves the auxiliary judgment ability and maintenance efficiency of ERMS radiation abnormality discovery.